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Table 3 Compound-gene bioactivity prediction

From: edge2vec: Representation learning using edge semantics for biomedical knowledge discovery

Algorithm Precision Recall F1 measure Hamming loss AUROC
DeepWalk 0.7787 0.7750 0.7742 0.2250 0.7660
LINE 0.8170 0.8166 0.8166 0.1833 0.8058
node2vec 0.7983 0.7916 0.7904 0.2083 0.7793
metapath2vec (Co-Ge-Co) 0.5170 0.5170 0.5168 0.4830 0.5007
metapath2vec (Co-Ge-Ge-Co) 0.4979 0.4980 0.4976 0.5020 0.4890
metapath2vec (Co-Dr-Ge-Dr-Co) 0.5305 0.5305 0.5304 0.4695 0.5304
metapath2vec++ (Co-Ge-Co) 0.4969 0.4970 0.4965 0.5030 0.4776
metapath2vec++ (Co-Ge-Ge-Co) 0.4854 0.4855 0.4854 0.5145 0.4776
metapath2vec++ (Co-Dr-Ge-Dr-Co) 0.5120 0.5120 0.5119 0.4880 0.5102
edge2vec 0.9017* 0.9000* 0.8998* 0.1000* 0.8914*
  1. Symbol “*” highlights the cases where our model significantly beats the best baseline with p value smaller than 0.01